Publikationen von U von Luxburg

Maier, M.; von Luxburg, U.; Hein, M.: How the result of graph clustering methods depends on the construction of the graph. ESAIM: Probability and Statistics 17, S. 370 - 418 (2013)

Bubeck, S.; Meila, M.; von Luxburg, U.: How the initialization affects the stability of the қ-means algorithm. ESAIM: Probability and Statistics 16, S. 436 - 452 (2012)

von Luxburg, U.: Clustering stability: an overview. Foundations and Trends in Machine Learning 2 (3), S. 235 - 274 (2010)

von Luxburg, U.; Franz, V.: A Geometric Approach to Confidence Sets for Ratios: Fieller‘s Theorem, Generalizations, and Bootstrap. Statistica Sinica 19 (3), S. 1095 - 1117 (2009)

Maier, M.; Hein, M.; von Luxburg, U.: Optimal construction of k-nearest-neighbor graphs for identifying noisy clusters. Theoretical computer science 410 (19), S. 1749 - 1764 (2009)

Bubeck, S.; von Luxburg, U.: Nearest Neighbor Clustering: A Baseline Method for Consistent Clustering with Arbitrary Objective Functions. The Journal of Machine Learning Research 10, S. 657 - 698 (2009)

von Luxburg, U.; Belkin, M.; Bousquet, O.: Consistency of Spectral Clustering. The Annals of Statistics 36 (2), S. 555 - 586 (2008)

von Luxburg, U.: A Tutorial on Spectral Clustering. Statistics and Computing 17 (4), S. 395 - 416 (2007)

Hein, M.; Audibert, J.-Y.; von Luxburg, U.: Graph Laplacians and their Convergence on Random Neighborhood Graphs. The Journal of Machine Learning Research 8, S. 1325 - 1370 (2007)

von Luxburg, U.; Bousquet, O.: Distance-Based Classification with Lipschitz Functions. The Journal of Machine Learning Research 5, S. 669 - 695 (2004)

von Luxburg, U.; Bousquet, O.; Schölkopf, B.: A Compression Approach to Support Vector Model Selection. The Journal of Machine Learning Research 5, S. 293 - 323 (2004)

von Luxburg, U.: Statistical Learning with Similarity and Dissimilarity Functions. Logos Verlag, Berlin, Germany (2004), 166 S.

von Luxburg, U.; Schölkopf, B.: Statistical Learning Theory: Models, Concepts, and Results. In: Handbook of the History of Logic, Bd. 10: Inductive Logic, S. 651 - 706 (Hg. Gabbay, M.; Hartmann, S.; Woods, J.). Elsevier North Holland, Amsterdam, Netherlands (2011)

Kakade, S.; von Luxburg, U. (Hg.): 24th Annual Conference on Learning Theory, 9-11 June 2011, Budapest, Hungary. 24th Annual Conference on Learning Theory (COLT 2011), Budapest, Hungary, 09. Juli 2011 - 11. Juli 2011. MIT Press, Cambridge, MA, USA (2011), 834 S.

Bousquet, O.; von Luxburg, U.; Rätsch, G. (Hg.): Advanced Lectures on Machine Learning: ML Summer Schools 2003, Canberra, Australia, February 2 - 14, 2003, Tübingen, Germany, August 4 - 16, 2003 (Lecture Notes in Computer Science, 3176). Machine Learning Summer School (MLSS 2003), Canberra, Australia, 02. Februar 2004 - 14. Februar 2004. Springer, Berlin, Germany (2004), 240 S.

Alamgir, M.; von Luxburg, U.: Phase transition in the family of p-resistances. In: Advances in Neural Information Processing Systems 24, S. 379 - 387 (Hg. Shawe-Taylor, J.; Zemel, R.; Bartlett, P.; Pereira, F.; Weinberger, K.). Twenty-Fifth Annual Conference on Neural Information Processing Systems (NIPS 2011), Granada, Spain. Curran, Red Hook, NY, USA (2012)

García-García, D.; von Luxburg, U.; Santos-Rodríguez, R.: Risk-Based Generalizations of f-divergences. In: 28th International Conference on Machine Learning (ICML 2011), S. 417 - 424 (Hg. Getoor, L.). 28th International Conference on Machine Learning (ICML 2011), Bellevue, WA, USA. International Machine Learning Society, Madison, WI, USA (2011)

Kpotufe, S.; von Luxburg, U.: Pruning nearest neighbor cluster trees. In: 28th International Conference on Machine Learning (ICML 2011), S. 225 - 232 (Hg. Getoor, L.; Scheffer, T.). 28th International Conference on Machine Learning (ICML 2011), Bellevue, WA, USA. International Machine Learning Society, Madison, WI, USA (2011)

von Luxburg, U.; Radl, A.; Hein, M.: Getting lost in space: Large sample analysis of the resistance distance. Twenty-Fourth Annual Conference on Neural Information Processing Systems (NIPS 2010), Vancouver, BC, Canada, 06. Dezember 2010 - 11. Dezember 2010. Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, S. 2622 - 2630 (2011)

Alamgir, M.; von Luxburg, U.: Multi-agent random walks for local clustering. In: IEEE International Conference on Data Mining (ICDM 2010), S. 18 - 27. IEEE International Conference on Data Mining (ICDM 2010), Sydney, Australia. IEEE, Piscataway, NJ, USA (2010)